# Normative Modeling via Conditional Variational Autoencoder and Adversarial Learning to Identify Brain Dysfunction in Alzheimer's Disease

arXiv: https://arxiv.org/abs/2211.08982 · HTML: https://mlsanity.com/paper/2211.08982/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 13 Nov 2022.

## Builds on (most cited first, 3 of 3)

- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations](https://mlsanity.com/paper/1805.00328/) (2018)

## Built on by (most cited first, 2 of 2)

- [Normative Diffusion Autoencoders: Application to Amyotrophic Lateral Sclerosis](https://mlsanity.com/paper/2407.14191/) (2024)
- [Normative Modeling for AD Diagnosis and Biomarker Identification](https://mlsanity.com/paper/2411.10570/) (2024)

## Abstract (arXiv)

Normative modeling is an emerging and promising approach to effectively study disorder heterogeneity in individual participants. In this study, we propose a novel normative modeling method by combining conditional variational autoencoder with adversarial learning (ACVAE) to identify brain dysfunction in Alzheimer's Disease (AD). Specifically, we first train a conditional VAE on the healthy control (HC) group to create a normative model conditioned on covariates like age, gender and intracranial volume. Then we incorporate an adversarial training process to construct a discriminative feature space that can better generalize to unseen data. Finally, we compute deviations from the normal criterion at the patient level to determine which brain regions were associated with AD. Our experiments on OASIS-3 database show that the deviation maps generated by our model exhibit higher sensitivity to AD compared to other deep normative models, and are able to better identify differences between the AD and HC groups.